Intelligent Management and Artificial Intelligence: Trends, Challenges, and Opportunities, Vol.1

Purpose: The article examines the application of artificial intelligence (AI) in the design and implementation of social research, with a focus on the processes of conceptualization and operationalization, empirical data collection, and research ethics. Need for the study: Despite the growing interest in AI in the social sciences, there are still research gaps regarding the use of modern technologies, such as natural language processing (NLP) and machine learning, in the research process. Key challenges include the interpretability of AI models, the risk of algorithmic bias, and the integration of AI with classic qualitative and quantitative research methods. Methodology: The article is based on an analysis of literature and case studies on the use of AI in social research, including both an overview of AI tools and an analysis of their potential applications and limitations. Findings: AI significantly supports researchers at various stages of the research process, from formulating research questions to analyzing data to coordinating field research. At the same time, significant challenges are identified, such as the lack of transparency of algorithms, the risk of automating biases, and the dehumanization of the research process. Practical Implications: The integration of AI in the social sciences requires the development of methodological standards and ethical regulations. Researchers should combine AI with traditional research methods to minimize the risk of errors and ensure the reliability of analyses.